3D-CNN MVS Depth Estimation.    Learning-based MVS methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} rely on traditional plane-sweeping [9]}, [10]} to generate a cost volume by associating reference frame and source frames for similarity matching, followed by encoder-decoder architectures for cost volume aggregation and depth map prediction. Among them, MVSNet [7]}, R-MVSNet [8]} and DPSNet [13]}, leverage 3D convolutions to regularize 4D cost volumes and regress depth maps via softargmin [14]}. Different strategies have been introduced for cost volumes construction. MVSNet [7]} proposes a variance-based cost volume for multi-view similarity measurement. Cas-MVSNet [16]} builds cascade cost volumes based on multi-scale feature pyramid and regresses the depth map in a multi-stage coarse-to-fine manner. Similarly, Cheng et al. propose UCS-Net [17]} to build cascade adaptive thin volumes by leveraging variance-based uncertainty. CVP-MVSNet [6]} builds a cost volume pyramid via multi-scale images to reduce to memory footprint. This cost volume mechanism is also adopted to other related tasks, e.g., 3D plane reconstruction in PlaneMVS [19]}, which proposes to leverage slanted plane sweeping to help plane reconstruction, achieving accurate depth predictions.
